Data Characteristics

The dataset encompasses crucial economic information for each country and continent worldwide, spanning five-year intervals from 1962 to 2007. This data pertains to:

  1. GDP indicators (such as GDP per capita, exports, imports, contributions from various sectors like industry, agriculture, services, and the financial sector, as well as inflation rates).
  2. Energy metrics (including electric power consumption and overall energy usage).
  3. CO2 emissions data.
  4. Societal metrics like life expectancy and fertility rates.
  5. Population statistics, including total population and population density.

CO2 Emissions vs. GDP per Capita

Country.Name Year Agriculture..value.added….of.GDP. CO2.emissions..metric.tons.per.capita. Domestic.credit.provided.by.financial.sector….of.GDP. Electric.power.consumption..kWh.per.capita. Energy.use..kg.of.oil.equivalent.per.capita. Exports.of.goods.and.services….of.GDP. Fertility.rate..total..births.per.woman. GDP.growth..annual… Imports.of.goods.and.services….of.GDP. Industry..value.added….of.GDP. Inflation..GDP.deflator..annual… Life.expectancy.at.birth..total..years. Population.density..people.per.sq..km.of.land.area. Services..etc…value.added….of.GDP. pop continent gdpPercap
Afghanistan 1962 NA 0.0737813 21.276422 NA NA 4.878051 7.450 NA 9.349593 NA NA 33.21990 14.31206 NA 10267083 Asia 853.1007
Afghanistan 1967 NA 0.1237824 9.917662 NA NA 6.772908 7.450 NA 14.209827 NA NA 35.38941 15.88181 NA 11537966 Asia 836.1971
Afghanistan 1972 NA 0.1308201 18.880833 NA NA 14.763231 7.450 NA 18.105850 NA NA 37.61015 17.94703 NA 13079460 Asia 739.9811
Afghanistan 1977 NA 0.1831183 13.836822 NA NA 11.662904 7.449 NA 14.823175 NA NA 40.11015 19.99893 NA 14880372 Asia 786.1134
Afghanistan 1982 NA 0.1658791 NA NA NA NA 7.450 NA NA NA NA 43.23073 19.40232 NA 12881816 Asia 978.0114
Afghanistan 1987 NA 0.2755603 NA NA NA NA 7.461 NA NA NA NA 47.29634 17.36656 NA 13867957 Asia 852.3959

The correlation between CO2 emissions (metric tons per capita) and GDP is highly significant, demonstrating a robust statistical strength, as evidenced by a correlation coefficient of 0.93 and an almost negligible p-value.

## [1] "Correlation between CO2 emissions and GDP is:0.926 with a p-value:1.12867922100394e-46"

## The year with the strongest correlation between CO2 Emmissions
##     and GDP Per Capita is: 1967

Interactive plot

## Relationship Between Continent and Energy use (kg of oil equivalent per capita)

To understand the relationship between the categorical variable (“continent”) and the continuous variable (“Energy use (kg of oil equivalent per capita)”), we can use analysis of variance (ANOVA) or perform a visual analysis using violin plots.

## 3.732041e-21

This extremely low p-value indicates that there is a statistically significant relationship between”continent” and “Energy use (kg of oil equivalent per capita).”

Comparing Europe and Asia’s ‘Imports of goods and services (% of GDP)’ after 1990


Since there’s only one measurement available for each country per year, the total importation used in Europe and Asia remains independent. Given this independence, we can choose a paired t-test to analyze any potential differences between the energy usage in the two regions.

## 
##  Welch Two Sample t-test
## 
## data:  europe_data and asia_data
## t = 0.28512, df = 4.7615, p-value = 0.7875
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -352.9054  439.4718
## sample estimates:
## mean of x mean of y 
##  1190.993  1147.710

Due to the high p-value obtained from the t-test, we can conclude that there no substantial and statistically significant distinctions in the total importations of goods and services between Europe and Asia, spanning the time frame from 1990 onward.

Identifying The Country With the largest ‘Population Density’ Across All Years